Knowledge discovery from social media and biomedical literature for adverse drug events

Abstract

In adverse drug event (ADE) monitoring and reporting, drug-related messages (60) are detected in one or more social media message streams as messages that include a name of a monitored drug. ADE reports (62) are extracted from the drug-related messages using an ADE classifier (46). The extracted ADE reports are validated by comparison with known ADEs of the monitored drug stored in an ADE knowledge base (64). Extracted ADE reports that fail the validating are collected in a non-validated ADE reports database (72). A report (74) is generated including information on at least one previously unrecognized ADE for which extracted ADE reports in the non-validated ADE reports database satisfy a previously unrecognized ADE criterion (in terms of number of messages or number of unique patients reporting the ADE).

1 . An adverse drug event (ADE) monitoring and reporting device comprising:
 a computer programmed to perform an ADE monitoring and reporting method including:
 detecting drug-related messages in one or more social media message streams as messages that include a name of a monitored drug; 
 extracting ADE reports from the drug-related messages using an ADE classifier, wherein the ADE classifier comprises a convolutional neural network (CNN) classifier trained on n-grams extracted from messages from the one or more social media message streams to classify the messages as to whether they report an ADE using the n-grams as features; 
 validating the extracted ADE reports by comparison with known ADEs of the monitored drug stored in an ADE knowledge base; 
 collecting extracted ADE reports that fail the validating in a non-validated ADE reports database; and 
 generating a report including information on at least one previously unrecognized ADE for which extracted ADE reports in the non-validated ADE reports database satisfies a previously unrecognized ADE criterion. 
   
     
     
         2 . The ADE monitoring and reporting device of  claim 1  wherein the ADE monitoring and reporting method the computer is programmed to perform further includes:
 tuning the ADE classifier using extracted ADE reports that pass the validating while not tuning the ADE classifier using extracted ADE reports that fail the validating. 
 
     
     
         3 . The ADE monitoring and reporting device of  claim 1  wherein the ADE monitoring and reporting method the computer is programmed to perform further includes:
 grouping ADE reports that pass the validating by known ADE; 
 wherein the generated report further includes information on relative occurrence frequencies of known ADEs in the ADE reports that pass the validating. 
 
     
     
         4 . The ADE monitoring and reporting device of  claim 3  wherein the extracted ADE reports include identification of patients receiving the monitored drug and the relative occurrence frequencies of known ADEs are for unique patients receiving the monitored drug. 
     
     
         5 . (canceled) 
     
     
         6 . The ADE monitoring and reporting device of  claim 1  wherein the ADE classifier is trained to detect ADEs represented by ADE terminology including lay terms for ADEs. 
     
     
         7 . The ADE monitoring and reporting device of  claim 1  wherein:
 the extracting includes extracting ADE n-grams representing ADEs from the drug-related messages; and 
 the validating includes identifying the ADE n-grams in the ADE knowledge base. 
 
     
     
         8 . The ADE monitoring and reporting device of  claim 1  wherein the previously unrecognized ADE criterion comprises the number of unique patients having at least one non-validated ADE report indicating the previously unrecognized ADE in the non-validated ADE reports database exceeding a threshold. 
     
     
         9 . The ADE monitoring and reporting device of  claim 1  wherein the previously unrecognized ADE criterion comprises the number of non-validated ADE reports indicating the previously unrecognized ADE in the non-validated ADE reports database exceeding a threshold. 
     
     
         10 . The ADE monitoring and reporting device of  claim 1  wherein the detecting includes:
 detecting drug-related messages from the one or more social media message streams as messages that include any of a plurality of names of the monitored drug. 
 
     
     
         11 . A non-transitory storage medium storing instructions readable and executable by a computer to perform an adverse drug event (ADE) monitoring and reporting method for a monitored drug having a set of known ADEs, the method comprising:
 identifying drug-related messages in one or more social media message streams wherein each drug-related message includes a name of the monitored drug;   extracting ADE reports from the drug-related messages by classification of the drug-related messages using n-grams extracted from the drug-related messages as features of an ADE classifier, wherein the ADE classifier comprises a convolutional neural network (CNN) classifier trained on n-grams extracted from messages from the one or more social media message streams; and   identifying a previously unrecognized ADE that is not in the set of known ADEs for the monitored drug in response to an accumulation of extracted ADE reports indicating the previously unrecognized ADE.   
     
     
         12 . The non-transitory storage medium of  claim 11  wherein:
 the extracting includes extracting patients who are subjects of the ADE reports; and 
 the identifying comprises identifying the previously unrecognized ADE in response to an accumulation of extracted ADE reports indicating the previously unrecognized ADE for at least a threshold number of different patients. 
 
     
     
         13 . The non-transitory storage medium of  claim 11  wherein the identifying comprises identifying the previously unrecognized ADE in response to the number of extracted ADE reports indicating the previously unrecognized ADE exceeding a threshold. 
     
     
         14 . (canceled) 
     
     
         15 . (canceled) 
     
     
         16 . (canceled) 
     
     
         17 . (canceled) 
     
     
         18 . An adverse drug event (ADE) monitoring and reporting method performed for a monitored drug, the method comprising:
 identifying drug-related messages that include a name of the monitored drug;   extracting ADE reports from the identified ADE reporting messages by classifying text of the drug-related messages using an ADE classifier, wherein the ADE classifier comprises a convolutional neural network (CNN) classifier trained on n-grams extracted from messages from the one or more social media message streams to classify the messages as to whether they report an ADE using the n-grams as features; and   outputting a report on the extracted ADE reports.   
     
     
         19 . (canceled) 
     
     
         20 . (canceled) 
     
     
         21 . (canceled)